The field of the invention is magnetic resonance imaging (“MRI”) methods and systems. More particularly, the invention relates to improving net acceleration when performing parallel imaging using inversion recovery pulse sequences.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or “spins”, after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically proven pulse sequences and they also enable the development of new pulse sequences.
Depending on the technique used, many MR scans currently used to produce medical images require many minutes to acquire the necessary data. The reduction of this scan time is an important consideration, since reduced scan time increases patient throughout, improves patient comfort, and improves image quality by reducing motion artifacts. Many different strategies have been developed to shorten the scan time.
One such strategy is referred to generally as “parallel imaging.” Parallel imaging techniques use spatial information from arrays of RF receiver coils to substitute for the encoding that would otherwise have to be obtained in a sequential fashion using RF pulses and field gradients (such as phase and frequency encoding). Each of the spatially independent receiver coils of the array carries certain spatial information and has a different sensitivity profile. This information is utilized in order to achieve a complete location encoding of the received MR signals by a combination of the simultaneously acquired data received from the separate coils. Specifically, parallel imaging techniques undersample k-space by reducing the number of acquired phase-encoded k-space sampling lines while keeping the maximal extent covered in k-space fixed. The combination of the separate MR signals produced by the separate receiver coils enables a reduction of the acquisition time required for an image (in comparison to conventional k-space data acquisition) by a factor that in the most favorable case equals the number of the receiver coils. Thus the use of multiple receiver coils acts to multiply imaging speed, without increasing gradient switching rates or RF power.
Two categories of such parallel imaging techniques that have been developed and applied to in vivo imaging are SENSE (SENSitivity Encoding) and SMASH (SiMultaneous Acquisition of Spatial Harmonics). With SENSE, the undersampled k-space data is first Fourier transformed to produce an aliased image from each coil, and then the aliased image signals are unfolded by a linear transformation of the superimposed pixel values. With SMASH, the omitted k-space lines are filled in or reconstructed prior to Fourier transformation, by constructing a weighted combination of neighboring lines acquired by the different receiver coils. Both SENSE and SMASH require that the spatial sensitivity of the coils be determined, and one way to do so is by “autocalibration” that entails the use of variable density k-space sampling.
A more recent advance to SMASH techniques using autocalibration is a technique known as GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions), introduced by Griswold et al. This technique is described in U.S. Pat. No. 6,841,998 as well as in the article titled “Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA),” by Griswold et al. and published in Magnetic Resonance in Medicine 47:1202-1210 (2002). Using these GRAPPA techniques, lines near the center of k-space are sampled at the Nyquist frequency (in comparison to the greater spaced lines at the edges of k-space). These so-called autocalibration signal (ACS) lines are then used to determine the weighting factors that are used to reconstruct the missing k-space lines. In particular, a linear combination of individual coil data is used to create the missing lines of k-space. The coefficients for the combination are determined by fitting the acquired data to the more highly sampled data near the center of k-space.
When performing parallel imaging it is typically necessary to determine the sensitivity profiles of receiver coils. Traditionally this is done by one of two methods. First, a separate calibration scan can be performed, but this reduces the overall efficiency of an MRI examination because the time gains achieved by the parallel imaging process are offset by the addition of the calibration scan. Second, a self-calibration process can be performed. In this case, the data necessary to perform self-calibration is acquired by fully sampling data from a central region of otherwise undersampled k-space, which extends or repeats the imaging pulse sequences. For example, in FIG. 1a, an imaging process using traditional self-calibration begins at process block 2 with the acquisition of image data using parallel imaging techniques. This image acquisition process is performed by applying an imaging pulse sequence. At process block 4, calibration data is acquired by extending the imaging pulse sequence or by performing additional repetitions of the imaging pulse sequence. Following data acquisition, at process block 6, coil sensitivity profiles are generated using the calibration data and employed to perform image reconstruction at process block 8. In such a scan, the portion of acquisition time devoted to acquiring calibration data can be substantial and result in significantly reduced net acceleration. The reduction of net acceleration is especially prevalent at high acceleration factors and partially defeats the purpose of performing parallel imaging.
The reduction in net acceleration with traditional self-calibration methods can be modeled by considering a 3DFT acquisition with acceleration applied along two phase encoding directions, ky and kz, where the corners of ky-space and kz-space are not sampled so as to provide isotropic resolution in the y-z plane. This acquisition would include πN2/4R phase encodes, where R is a nominal undersampling factor that is the product of the undersampling factors in the two phase encoding directions, that is, R=Ry*Rz. A similar, unaccelerated scan would include πN2/4 phase encodes. If data necessary for self-calibration is acquired, then a central region of k-space is further sampled. Therefore, the total number of phase encodes (A) required for calibration is described by the following function:
                              A          =                                                                      π                  ⁢                                                                          ⁢                                      r                    c                    2                                    ⁢                                      N                    2                                                  4                            +                              (                                                                            π                      ⁢                                                                                          ⁢                                              N                        2                                                                                    4                      ⁢                                                                                          ⁢                      R                                                        -                                                            π                      ⁢                                                                                          ⁢                                              r                        c                        2                                            ⁢                                              N                        2                                                                                    4                      ⁢                                                                                          ⁢                      R                                                                      )                                      =                                                            π                  ⁢                                                                          ⁢                                      N                    2                                                                    4                  ⁢                                                                          ⁢                  R                                            ⁢                              (                                  1                  +                                                            r                      c                      2                                        ⁡                                          (                                              R                        -                        1                                            )                                                                      )                                                    ;                            Eqn        .                                  ⁢        1            
where rc defines the fraction of k-space radius acquired for performing calibration. From this equation it possible to determine that the net acceleration (Rnet), which equals the number of phase encodes in an accelerated scan versus an unaccelerated scan, is R/(1+rc2(R−1). As shown in FIG. 1b, this can lead to large differences between the net acceleration factor, which is actually attained, and the nominal acceleration factor.
It would therefore be desirable to develop systems and methods for acquiring the data necessary for calibration without reducing the net acceleration factor or adding to the overall scanning time.